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Institute
- Institut für Geowissenschaften (67) (remove)
Taking into account the climatic conditions of the semiarid region of Brazil, with its intermittent rivers and long periods of water scarcity, a dense network of surface reservoirs (on average one dam every 5 km(2)) of very different sizes has been built. The impact of such a network on water and sediment dynamics constitutes a remarkable challenge for hydrologists. The main objective of this work is to present a novel way of simulating water and sediment fluxes through such high-density reservoir networks, which enables the assessment of water and sediment retention in those structures. The new reservoir modeling approach has been coupled with the fully process-oriented and semidistributed hydrological WASA-SED model, which was tailored for semiarid hydroclimatological characteristics. This integrated modeling system was applied to the 933-km(2) Bengue catchment, located in semiarid northeastern Brazil, which has a network of 114 reservoirs with a wide range of surface areas (from 0.003 to 350 ha). The small reservoirs were grouped into size classes according to their storage capacity and a cascade routing scheme was applied to describe the upstream-downstream position of the classes; the large reservoirs were handled explicitly in the reservoir modeling approach. According to the model results, the proposed approach is capable of representing the water and sediment fluxes though the entire reservoir network with reasonable accuracy. In addition, the model shows that the dynamics of water and sediment within the Bengue catchment are strongly impacted by the presence of multiple reservoirs, which are able to retain approximately 21% of the generated runoff and almost 42% of the sediment yield of the catchment for the simulation period, from 2000 to 2012. (C) 2018 American Society of Civil Engineers.
Modelers can improve a model by addressing the causes for the model errors (data errors and structural errors). This leads to implementing model enhancements (MEs), for example, meteorological data based on more monitoring stations, improved calibration data, and/or modifications in process formulations. However, deciding on which MEs to implement remains a matter of expert knowledge. After implementing multiple MEs, any improvement in model performance is not easily attributed, especially when considering different objectives or aspects of this improvement (e.g., better dynamics vs. reduced bias). We present an approach for comparing the effect of multiple MEs based on real observations and considering multiple objectives (MMEMO). A stepwise selection approach and structured plots help to address the multidimensionality of the problem. Tailored analyses allow a differentiated view on the effect of MEs and their interactions. MMEMO is applied to a case study employing the mesoscale hydro-sedimentological model WASA-SED for the Mediterranean-mountainous Isabena catchment, northeast Spain. The investigated seven MEs show diverse effects: some MEs (e.g., rainfall data) cause improvements for most objectives, while other MEs (e.g., land use data) only affect a few objectives or even decrease model performance. Interaction of MEs was observed for roughly half of the MEs, confirming the need to address them in the analysis. Calibration and increasing the temporal resolution showed by far stronger impact than any of the other MEs. The proposed framework can be adopted in other studies to analyze the effect of MEs and, thus, facilitate the identification and implementation of the most promising MEs for comparable cases.
Many semi-arid regions are characterised by water scarcity and vulnerability of natural resources, pronounced climatic variability and social stress. Integrated studies including climatotogy, hydrology, and socio-econornic studies are required both for analysing the dynamic natural conditions and to assess possible strategies to make semi-arid regions Less vulnerable to the present and changing climate. The model introduced here dynamically describes the retationships between climate forcing, water availability, agriculture and selected societal processes. The model has been tailored to simulate the rather complex situation in the semi-and north-eastern Brazil in a quantitative manner including the sensitivity to external forcing, such as climate change. The selected results presented show the general functioning of the integrated model, with a primary focus on climate change impacts. It becomes evident that due to Large differences in regional climate scenarios, it is still impossible to give quantitative values for the most probable development, e.g., to assign probabilities to the simulated results. However, it becomes clear that water is a very crucial factor, and that an efficient and ecologically sound water management is a key question for the further development of that semi-arid region. The simulation results show that, independent of the differences in climate change scenarios, rain-fed farming is more vulnerable to drought impacts compared to irrigated farming. However, the capacity of irrigation and other water infrastructure systems to enhance resilience in respect to climatic fluctuations is significantly constrained given a significant negative precipitation trend. (c) 2005 Elsevier B.V. All rights reserved.
Scenario-neutral response surfaces illustrate the sensitivity of a simulated natural system, represented by a specific impact variable, to systematic perturbations of climatic parameters. This type of approach has recently been developed as an alternative to top-down approaches for the assessment of climate change impacts. A major limitation of this approach is the underrepresentation of changes in the temporal structure of the climate input data (i.e., the seasonal and day-to-day variability) since this is not altered by the perturbation. This paper presents a framework that aims to examine this limitation by perturbing both observed and projected climate data time series for a future period, which both serve as input into a hydrological model (the HBV model). The resulting multiple response surfaces are compared at a common domain, the standardized runoff response surface (SRRS). We apply this approach in a case study catchment in Norway to (i) analyze possible changes in mean and extreme runoff and (ii) quantify the influence of changes in the temporal structure represented by 17 different climate input sets using linear mixed-effect models. Results suggest that climate change induced increases in mean and peak flow runoff and only small changes in low flow. They further suggest that the effect of the different temporal structures of the climate input data considerably affects low flows and floods (at least 21% influence), while it is negligible for mean runoff.
The characteristics of a landscape pose essential factors for hydrological processes. Therefore, an adequate representation of the landscape of a catchment in hydrological models is vital. However, many of such models exist differing, amongst others, in spatial concept and discretisation. The latter constitutes an essential pre-processing step, for which many different algorithms along with numerous software implementations exist. In that context, existing solutions are often model specific, commercial, or depend on commercial back-end software, and allow only a limited or no workflow automation at all. Consequently, a new package for the scientific software and scripting environment R, called lumpR, was developed. lumpR employs an algorithm for hillslope-based landscape discretisation directed to large-scale application via a hierarchical multi-scale approach. The package addresses existing limitations as it is free and open source, easily extendible to other hydrological models, and the workflow can be fully automated. Moreover, it is user-friendly as the direct coupling to a GIS allows for immediate visual inspection and manual adjustment. Sufficient control is furthermore retained via parameter specification and the option to include expert knowledge. Conversely, completely automatic operation also allows for extensive analysis of aspects related to landscape discretisation. In a case study, the application of the package is presented. A sensitivity analysis of the most important discretisation parameters demonstrates its efficient workflow automation. Considering multiple streamflow metrics, the employed model proved reasonably robust to the discretisation parameters. However, parameters determining the sizes of subbasins and hillslopes proved to be more important than the others, including the number of representative hillslopes, the number of attributes employed for the lumping algorithm, and the number of sub-discretisations of the representative hillslopes.
Wetlands, and in particular riparian wetlands, represent an interface between the catchment area and the aquatic environment. They control the exchange of water and related chemical fluxes from the upper catchment area to surface waters like streams and lakes. Their influence on water and nutrient balances has been investigated mainly at the patch scale. In this study an attempt was made (a) to integrate riparian zones and wetlands into eco-hydrological river basin modelling, and (b) to quantify the impacts of riparian wetland processes on water and nutrient fluxes in a meso-scale catchment located in the northeastern German lowland. The investigation was performed by analysing hydro-chemical field data and applying the eco-hydrological model SWIM (Soil and Water Integrated Model), which was extended to reproduce the relevant water and nutrient flows and retention processes at the catchment scale in general, and in riparian zones and wetlands in particular. The main extensions introduced in the model were: (1) implementation of daily groundwater table dynamics at the hydrotope level, (2) implementation of water and nutrient uptake by plants from groundwater in riparian zones and wetlands, and (3) assessment of nutrient retention in groundwater and interflow. The simulation results indicate that wetlands, though they represent relatively small parts of the total catchment area, may have a significant impact on the overall water and nutrient balances of the catchment. The uncertainty of the simulation results is considerably high, with the main sources of uncertainty being the model parameters representing the geo-hydrology and the input data for land use management. (c) 2006 Elsevier B.V. All rights reserved.
This case study evaluates the suitability of radar-based quantitative precipitation estimates (QPEs) for the simulation of streamflow in the Marikina River Basin (MRB), the Philippines. Hourly radar-based QPEs were produced from reflectivity that had been observed by an S-band radar located about 90 km from the MRB. Radar data processing and precipitation estimation were carried out using the open source library wradlib. To assess the added value of the radar-based QPE, we used spatially interpolated rain gauge observations (gauge-only (GO) product) as a benchmark. Rain gauge observations were also used to quantify rainfall estimation errors at the point scale. At the point scale, the radar-based QPE outperformed the GO product in 2012, while for 2013, the performance was similar. For both periods, estimation errors substantially increased from daily to the hourly accumulation intervals. Despite this fact, both rainfall estimation methods allowed for a good representation of observed streamflow when used to force a hydrological simulation model of the MRB. Furthermore, the results of the hydrological simulation were consistent with rainfall verification at the point scale: the radar-based QPE performed better than the GO product in 2012, and equivalently in 2013. Altogether, we could demonstrate that, in terms of streamflow simulation, the radar-based QPE can perform as good as or even better than the GO product - even for a basin such as the MRB which has a comparatively dense rain gauge network. This suggests good prospects for using radar-based QPE to simulate and forecast streamflow in other parts of the Philippines where rain gauge networks are not as dense.
Storm runoff from the Marikina River Basin frequently causes flood events in the Philippine capital region Metro Manila. This paper presents and evaluates a system to predict short-term runoff from the upper part of that basin (380km(2)). It was designed as a possible component of an operational warning system yet to be installed. For the purpose of forecast verification, hindcasts of streamflow were generated for a period of 15 months with a time-continuous, conceptual hydrological model. The latter was fed with real-time observations of rainfall. Both ground observations and weather radar data were tested as rainfall forcings. The radar-based precipitation estimates clearly outperformed the raingauge-based estimates in the hydrological verification. Nevertheless, the quality of the deterministic short-term runoff forecasts was found to be limited. For the radar-based predictions, the reduction of variance for lead times of 1, 2 and 3hours was 0.61, 0.62 and 0.54, respectively, with reference to a no-forecast scenario, i.e. persistence. The probability of detection for major increases in streamflow was typically less than 0.5. Given the significance of flood events in the Marikina Basin, more effort needs to be put into the reduction of forecast errors and the quantification of remaining uncertainties.
This manuscript proposes a method to assess hydrological drought in semi-arid environments under high impoundment rate and applies it to the semi-arid Jaguaribe River basin in Brazil. It analyzes droughts (1) in the largest reservoir systems; (2) in the Upper Basin, considering 4744 reservoirs, 800 wells and almost 18,000 cisterns; and (3) in reservoirs of different sizes during multiyear droughts. Results show that the water demand is constrained in the basin; hydrological and meteorological droughts are often out of phase; there is a negative correlation between storage level and drought severity; and the small systems cannot cope with long-term droughts.